Skill UI

A comprehensive guide for authors responding to major or minor revisions from top-tier academic journals like MIS Quarterly. This skill teaches how to structure an auditable, point-by-point rebuttal letter, systematically addressing reviewer concerns, enhancing methodological rigor (e.g., common-method bias, identification), updating transparency materials, and adhering to strict formatting constraints.

This guide outlines the rigorous process for estimating, validating, and interpreting structural models in Marketing Science manuscripts. It covers advanced econometric techniques (GMM, MLE, Bayesian), demonstrating empirical identification, assessing model fit (in-sample/out-of-sample), and computing robust counterfactuals. It ensures the final results are statistically sound, economically plausible, and prepared for replication.

This guide details the structure and strategic approach for writing a comprehensive response letter and rebuttal after a journal's revise-and-resubmit (R&R) cycle. It instructs authors on how to structure point-by-point answers, strengthen identification arguments, address challenging counterfactuals, and reconcile conflicting reviewer demands, ensuring the submission is rigorous and professional.

A guide for transforming abstract marketing phenomena into rigorous, mathematically testable models. It covers both analytical (game-theoretic) and structural econometric approaches, emphasizing the specification of primitives, deriving equilibria, and establishing clear identification arguments for academic research.

A comprehensive guide detailing the methodological standards for conducting high-impact research in Manufacturing and Service Operations Management. It covers both analytical modeling (proofs, sensitivity) and empirical studies (identification, robustness), emphasizing the necessity of full data and code replicability for published findings.

A comprehensive guide for building robust causal identification strategies in empirical research, particularly for analyzing policy shocks. It provides best practices and checklist items for advanced econometrics methods like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), and Double Machine Learning (DML), helping researchers avoid common methodological pitfalls in top-tier academic publications.

A detailed guide for authors submitting to top-tier management journals, focusing on responding to reviewer comments (R&R). It provides a structured methodology for drafting point-by-point rebuttal letters, ensuring alignment between the revised manuscript and the written response. The guide emphasizes prioritizing text revision, strengthening identification, mechanism, and policy implications.

This router provides a structured workflow guide for writing and revising academic manuscripts, specifically tailored for top-tier journals like Management World. It helps users determine the next critical step—from topic selection and literature review to causal identification and policy implication—ensuring a cohesive and strategically sound paper structure.

A comprehensive workflow router designed to guide researchers through the entire process of preparing manuscripts for top-tier business journals, such as the Nankai Business Review. This guide does not replace specialized skills but provides a structured, stage-gated approach covering theory gap identification, hypothesis development, rigorous measurement validation (e.g., SEM, validity), contextualization, and strategic revision (R&R). It ensures that the paper meets the necessary standards for theoretical contribution and empirical depth.

This skill simulates the rigorous peer review process used in top-tier AI and machine learning conferences (like NeurIPS). It helps users diagnose potential weaknesses in a submission regarding quality, clarity, significance, ethical compliance, and reproducibility. It guides the identification of decision-relevant issues that ACs and reviewers are likely to raise, helping authors strategically plan responses for revision.

A comprehensive guide for selecting and defending appropriate research designs—including qualitative, quantitative, experimental, and computational methods—for organizational science manuscripts. It emphasizes matching the method to the theoretical contribution and level of analysis, rather than solely focusing on causal identification. Learn how to structure your argument, ensure transparency, and build robust mechanism evidence.

This guide provides comprehensive standards for packaging ubiquitous computing sensing artifacts and datasets for academic publication. It covers best practices for ensuring reproducibility, emphasizing cross-subject evaluation (LOSO), necessary de-identification protocols for human-subjects data, and structuring the submission package for rigorous peer review.